Bibliographic record
Abstract
The current interest in crossbreeding in the commercial dairy industry, even though it is quite limited, raises questions of breed utilization. Fewer than 5% of US dairy cattle are other than purebred or grade Holsteins. The large advantage of Holsteins for additive genetic merit for lactation milk yield is apparently responsible for this trend. Why, then, this interest in crossbreeding? The economic importance of traits such as reproduction, health, and survival in dairy production systems is likely the basis for the interest in crossbreeding, even though these traits are secondary to milk yield. Several US studies and a Canadian study confirmed that while several crossbred groups were equivalent to Holsteins for lactation milk yield, none were superior. Two crossbred groups in the Canadian study had lifetime yields, milk value, and net returns equivalent to Holsteins. In the New Zealand study, Friesian-Jersey reciprocal crossbreds were predicted to exceed Friesians in first-lactation fat yield. Crossbred performance is dictated by a combination of additive and nonadditive genetic effects. Evidence exists for direct. maternal, heterosis, and cytoplasmic maternal effects. Heterosis of 15 to 20% for lifetime traits was found in two studies. Results from previous crossbreeding studies have something to recommend for inclusion of Holstein, Ayrshire, Brown Swiss, and Jersey breeds in a crossbreeding scheme. However, multiple-generation lifetime performance on an array of purebreds and crossbreds under US condition does not exist. Full unique identification of individual animals, including breed composition, would permit the use of DHIA data to estimate additive and nonadditive genetic parameters for the traits recorded therein. Survival data from birth and health data would need to be fully recorded to provide complete data on lifetime performance. Self-propagation of crossbred replacements is mandatory if any crossbreeding system is to be successful. Based on current empirical data, a two-breed rotational crossing system appears to be the most viable system to maximize economic merit. The theoretical advantages of a three-breed rotational crossing system are clear, but the data to recommend the third breed and this system in practice are limited. Full-scale long-term breeding experiments or analysis of field data paired with a comprehensive modeling of alternative breed utilization strategies for US conditions are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".